Estimating controlled direct treatment effects on pain intensity using structural mean models.

Introduction: Without valid inclusion of concurrent analgesic use, the primary analyses of pain intensity in pain randomized controlled trials (RCTs) may produce diminished estimated treatment effects. Methods: We used contemporary causal inference methods to reanalyze RCT data examining the effect of epidural steroid injection (ESI) as an example of a pain treatment. Specifically, we define an "attributable to ESI estimand," which is the controlled direct effect of ESI. We used a simple composite pain intensity outcome, the QPAC Results: The treatment effect of ESI on leg pain intensity using strict ITT was -0.751 numeric rating scale points (95% confidence interval [CI]: -1.287 to -0.214). Estimates for the attributable to ESI estimand were -0.864 (95% CI: -3.207 to 1.478) for estimating equations, -0.935 (95% CI: -1.779 to 0.090) for g-estimation, -0.653 (95% CI: -1.218 to -0.089) for generalized method of moments, and -0.930 (95% CI: -1.508 to -0.352) for the QPAC1.5. Discussion: We illustrate how contemporary causal inference methods and alternative estimands can be used to account for concurrent analgesic use in pain RCTs in a manner that may result in larger treatment effects.